Detruncation of Clinical CT Scans Using a Discrete Algebraic Reconstruction Technique Prior

Successful image reconstruction in computed tomography (CT) relies on the completeness of the projections. If the patient does not fit in the field of measurement, the projections are truncated causing cupping artifacts in the image and a diminished field of view (FOV). In order to restore the CT values and extend the FOV, the projections have to be completed, for example via an extrapolation. The discrete algebraic reconstruction technique (DART) has shown its efficacy in reconstructing discrete images from insufficient raw data. In this work, we use DART images as a prior for projection completion of clinical CT scans. We compare our method to the conventional adaptive detruncation (ADT) and evaluate the RMSE inside and outside the FOV along with the Dice score.